title: An elemental model of retrospective revaluation without within-compound associations.
abstract: When retrospective revaluation phenomena (e.g., unovershadowing: AB+, then A-, then test B) were discovered, simple elemental models were at a disadvantage because they could not explain such phenomena. Extensions of these models and novel models appealed to within-compound associations to accommodate these new data. Here, we present an elemental, neural network model of conditioning that explains retrospective revaluation apart from within-compound associations. In the model, previously paired stimuli (say, A and B, after AB+) come to activate similar ensembles of neurons, so that revaluation of one stimulus (A-) has the opposite effect on the other stimulus (B) through changes (decreases) in the strength of the inhibitory connections between neurons activated by B. The ventral striatum is discussed as a possible home for the structure and function of the present model.
authors.affiliation: Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada, patrick.connor@dal.ca.
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Connor
authors.forename: Vincent M
authors.initials: VM
authors.lastname: Lolordo
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Trappenberg
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003214:Conditioning, Classical / Q000502:physiology*; D003463:Cues; D007266:Inhibition, Psychological; D008960:Models, Psychological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*